(109) Analysis of Patient-Specific Microbiome Profiles and Possible Association with Sexual Dysfunction After Cervical Dysplasia Treatment
Bibliographic record
Abstract
Abstract Introduction Electrocautery of the cervix during the loop electrosurgical excision procedure (LEEP) effectively treats cervical dysplasia (CD). Most patients do not report adverse symptoms post-operatively. However, a subset of patients has reported sexual issues and psychosexual sequalae, including some impact to their quality of life. The etiology of these symptoms has not been investigated, especially in a functional capacity. Moreover, there is some evidence that LEEP alters the cervical microbiome, from a small number of studies. Given the close anatomical relatedness of the pelvis, regions in close proximity to the cervix should also be considered, such as the vagina and urethra. Together, these three regions comprise the female urogenital tract (FUT), which should be examined for persistent inflammatory bacterial profiles post-LEEP. Correlations between LEEP patient-reported symptoms of psychological and sexual issues, and bacterial microbiome profiles, have not yet been investigated. Objective To analyze overall and individualized bacterial profiles (cervix, vagina, urethra) of patients with CD before and after treatment with LEEP and investigate associations with sexual functioning. Methods Women with typical female internal organs and external genitalia, undergoing LEEP treatment for CD, were recruited to participate in the study. Urethral samples, in addition to vaginal and cervical swabs, were collected immediately before treatment and 3 months post-treatment. Bacterial community analysis was characterized by 16S ribosomal RNA gene analysis. Self-report online surveys assessing demographics, medical history, and sexual function (FSFI) were completed by participants at the same time intervals. Chi square analyses and t-tests were conducted. Results Alpha diversity (observed species richness) revealed a significant decrease in species richness in the FUT microbiome post-LEEP (P < 0.05). Beta diversity demonstrated significant differences between the cervical, urinary, and vaginal microbiomes pre- and post-LEEP (P < 0.05). Lactobacillus was observed to be significantly higher in the cervical microbiome (P < 0.05), and Prevotella, Dialister, and Ruminococcus were significantly lower in the cervical microbiome (P < 0.05), when compared to the urinary and vaginal microbiomes pre- and post-LEEP. Analysis of individual microbiome patient profiles revealed individuals who showed discordant trends when compared to data summarizing averages. Namely, a subset of participants experienced a significant increase (P < 0.05) in pro-inflammatory bacteria post-LEEP with correlative changes in SD. These results suggest there may be a non-uniform healing response post-LEEP, and that individualized microbiome analysis could reveal regional dysbiosis that could be subsequently treated and managed. Conclusions A diverse inflammatory bacterial community characterizes CD in the FUT, and treatment with LEEP mostly returns the microbiomes to a healthy state. However, some participants showed increased inflammatory bacterial profiles post-LEEP, potentially demonstrating a non-uniform healing response. This study provides a basis for future studies to screen and restore FUT microbiomes post-LEEP, with the aim of detecting and treating any bothersome symptoms reported by patients. Disclosure No
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".